Constant that multiplies the regularization term if regularization is
used. Defaults to 0.0001

fit_intercept:bool

Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.

max_iter:int, optional

The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the fit method, and not the
partial_fit.
Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.

New in version 0.19.

tol:float or None, optional

The stopping criterion. If it is not None, the iterations will stop
when (loss > previous_loss - tol). Defaults to None.
Defaults to 1e-3 from 0.21.

New in version 0.19.

shuffle:bool, optional, default True

Whether or not the training data should be shuffled after each epoch.

verbose:integer, optional

The verbosity level

eta0:double

Constant by which the updates are multiplied. Defaults to 1.

n_jobs:int or None, optional (default=None)

The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation.
None means 1 unless in a joblib.parallel_backend context.
-1 means using all processors. See Glossary
for more details.

The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by np.random.

early_stopping:bool, default=False

Whether to use early stopping to terminate training when validation.
score is not improving. If set to True, it will automatically set aside
a fraction of training data as validation and terminate training when
validation score is not improving by at least tol for
n_iter_no_change consecutive epochs.

New in version 0.20.

validation_fraction:float, default=0.1

The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True.

New in version 0.20.

n_iter_no_change:int, default=5

Number of iterations with no improvement to wait before early stopping.

Perceptron is a classification algorithm which shares the same
underlying implementation with SGDClassifier. In fact,
Perceptron() is equivalent to SGDClassifier(loss=”perceptron”,
eta0=1, learning_rate=”constant”, penalty=None).

Converts the coef_ member (back) to a numpy.ndarray. This is the
default format of coef_ and is required for fitting, so calling
this method is only required on models that have previously been
sparsified; otherwise, it is a no-op.

Classes across all calls to partial_fit.
Can be obtained by via np.unique(y_all), where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn’t need to contain all labels in classes.

sample_weight:array-like, shape (n_samples,), optional

Weights applied to individual samples.
If not provided, uniform weights are assumed.

Converts the coef_ member to a scipy.sparse matrix, which for
L1-regularized models can be much more memory- and storage-efficient
than the usual numpy.ndarray representation.

The intercept_ member is not converted.

Returns:

self:estimator

Notes

For non-sparse models, i.e. when there are not many zeros in coef_,
this may actually increase memory usage, so use this method with
care. A rule of thumb is that the number of zero elements, which can
be computed with (coef_==0).sum(), must be more than 50% for this
to provide significant benefits.

After calling this method, further fitting with the partial_fit
method (if any) will not work until you call densify.